ArticleDatabase : the journal of biological databases and curation2022
SLOAD: a comprehensive database of cancer-specific synthetic lethal interactions for precision cancer therapy via multi-omics analysis.
Article in Database : the journal of biological databases and curation, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 17 citations in OpenAlex.
- Role of Spatial Heterogeneity in Muscle-Invasive Bladder Cancer on Overall Survival and Immunotherapy Response.Cancers · 2026Article
- MVGAE: A Multi-View Graph Auto-Encoder Model for Drug Prediction of Non-Small Cell Lung Cancer Based on Synthetic Lethality.Current issues in molecular biology · 2026Article
- Article
- TARGET-SL: precision essential gene prediction using driver prioritisation and synthetic lethality.Briefings in bioinformatics · 2025Article
- Perspectives on cancer therapy-synthetic lethal precision medicine strategies, molecular mechanisms, therapeutic targets and current technical challenges.Cell death discovery · 2025Review
- CDK9 inhibitor elicits APC through a synthetic lethal effect in colorectal cancer cells.Genes & diseases · 2025Article
Corrections and comments
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Authors and funding
9 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Synthetic lethality has been widely concerned because of its potential role in cancer treatment, which can be harnessed to selectively kill cancer cells via identifying inactive genes in a specific cancer type and further targeting the corresponding synthetic lethal partners. Herein, to obtain cancer-specific synthetic lethal interactions, we aimed to predict genetic interactions via a pan-cancer analysis from multiple molecular levels using random forest and then develop a user-friendly database. First, based on collected public gene pairs with synthetic lethal interactions, candidate gene pairs were analyzed via integrating multi-omics data, mainly including DNA mutation, copy number variation, methylation and mRNA expression data. Then, integrated features were used to predict cancer-specific synthetic lethal interactions using random forest. Finally, SLOAD (http://www.tmliang.cn/SLOAD) was constructed via integrating these findings, which was a user-friendly database for data searching, browsing, downloading and analyzing. These results can provide candidate cancer-specific synthetic lethal interactions, which will contribute to drug designing in cancer treatment that can promote therapy strategies based on the principle of synthetic lethality. Database URL http://www.tmliang.cn/SLOAD/.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.